On Fri, 25 Jun 2010, bruno Piguet wrote:
Hi all,
I'm looking for a function which could do some fast and simple
re-sampling of asynchronous time series.
Below is a MCE of the kind of algorithm I need. As you can see, it's
quite crude, but it's enough for my current needs. The only problem is that
it is quite slow on real use case.
I've got a C version which is much faster, but I'd like to have a pure-R
program.
Any pointer to the relevant part of the doc one one of the time-series
packages ? Any suggestion or advice ?
Thanks in advance,
B. Piguet.
Here is the exemple :
Tx <- seq(1, 50, 0.5)
Tx <- Tx + rnorm(length(Tx), 0, 0.1)
X <- sin(Tx/10.0) + sin(Tx/5.0) + rnorm(length(Tx), 0, 0.1)
Ty <- seq(1, 50, 0.3333)
Ty <- Ty + rnorm(length(Ty), 0, 0.02)
Y <- sin(Ty/10.0) + sin(Ty/5.0) + rnorm(length(Ty), 0, 0.1)
w <- 0.25
Personally, I'd incline towards leaving the next lines to C, perhaps using
the inline package.
But if you want a purely R solution, the bioConductor IRanges package
should help. I think the viewMeans() function will handle this loop.
See
http://comments.gmane.org/gmane.comp.lang.r.sequencing/1296
for some discussion.
HTH,
Chuck
Y_sync <- rep(NA, length(Tx))
for (i in 1:length(Tx))
{
T_min <- Tx[i] - w
T_max <- Tx[i] + w
Y_sync[i] <- mean(Y[Ty >= T_min & Ty <= T_max ])
}
diff = X - Y_sync
print(summary(diff))
print(summary(lm(Y_sync~X)))
plot (diff~Tx, type="l")
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